In our last session, I gave everyone the same task: ask your AI tool to summarize a single article in plain language. Every person got a factually accurate summary, but the style and depth of analysis varied widely from person to person. We tested this further by entering the same prompt multiple times. Each time, the output was slightly different. We copied and pasted everything into a shared document so we could compare side by side.
Later that day, I asked everyone to reopen the tool they’d used that morning and repeat the prompt that had worked best: summarize this article in plain language. The outputs shifted again. Some changed dramatically. One team member whose morning summary had been light and conversational received a much more formal, serious version in the afternoon.
I then asked Claude to compare the morning and afternoon versions of one summary. “In short, the morning version emphasizes practical implications and accessibility; the afternoon version prioritizes completeness and specificity. They complement each other well — someone reading both would get a fuller picture than either one alone provides.”
Why would Claude produce these differences despite the same source material and prompt?
I asked the source.
Even with the same prompt and source material, LLMs don't produce identical outputs each time. This is by design — there's a degree of randomness (called "temperature") in how the model selects words, which means each run produces a slightly different path through the text.
But the differences here go beyond random word choices. The morning version landed on a journalistic structure while the afternoon version landed on an academic one. That likely reflects what the paper calls sensitivity to framing and order effects — one of the cognitive biases it catalogs. Small, invisible differences in how the model’s internal processing unfolds at the start of generation can cascade into meaningfully different organizational choices. Once the morning version opened with a news-style framing, the rest followed that pattern. Once the afternoon version opened with a descriptive academic tone, it followed that thread instead.
This is actually a concrete example of the robustness issues the paper identifies. The underlying reasoning about the paper’s content is consistent across both summaries — neither gets the facts wrong. But the presentation varies in ways that weren’t requested, which means someone relying on just one version would get a subtly incomplete picture depending on which run they happened to see.
It’s a useful reminder that LLM outputs are one plausible version of a response, not the definitive one.
It’s important to understand how these tools work. What looks like an authoritative answer is really just one of many possible outputs the system could have generated. The text will sound plausible whether it’s accurate or not, and you may get different results each time you ask.
My undergrad experience in college really shaped my approach to the internet. I was an English Education major at the time, in the early 00s, when the internet was around but mostly the wheelhouse of scholars and nerds. I was a nerd, learning code as a vehicle for writing, primarily to amuse myself and my friends.
The English department was an embattled unit within a school within a college of a STEM-centric university whose administration was perennially annoyed by the Humanities and their writing requirements. One of the English department’s survival tactics was to grow their approach to technical writing, getting deep into the question of how technology changes, shapes and shifts reading, writing and literacy. Thus they organized loosely around an emerging field called “digital rhetoric.”
For a time this was the top rhetoric and composition program in the field, populated by scholars from scrappy programs. My closest mentor, an English PhD from Wayne State in Detroit, studied race and gender representation in video games and how programmers (particularly Black and trans programmers) write themselves into existence through code, design and other aesthetic and storytelling choices. Outsiders had a really hard time understanding how this work belonged in an English department, but ultimately, she was focused on the question of authorship and how the author is projected throughout her work, a classic literary debate. She treated video games as texts and gamers as an audience, an approach that foretold many things about our current political era.
In this space, “digital” doesn’t just mean content on a screen. The concept is more complex, including social, cultural and rhetorical dimensions, in addition to shifts through time. Digital “texts” and practices exist on a continuum with print and other media, rather than in isolation, transforming how persuasion and communication work, both separately and together.
I took all these lessons and ran with them. This is where my approach to the internet is situated, and there are a few truisms that I learned from that time and era that further position my writing and approach.
Writing is code, code is writing
Writing and code are fundamentally the same thing in digital contexts. Both are systems of symbols that create meaning and action through semantic rules. The line between “content” and “container” blurs in digital spaces. A blog post is the copy on the page – and it’s also the metadata, the responsive design that adapts to different screens, the accessibility markup that makes it readable by screen readers. Each of these elements is written (coded) and each carries rhetorical weight and communicates something to the audience, intended or not. If you understand these relationships, you understand how the internet works as a social and information system.
This convergence of digital and material amplifies the concept of intertextuality, the idea that all texts reference, respond to and build upon other texts. In the classroom, intertextuality often focuses on plays and novels, and explores how authors speak and refer to one another’s work over time. In music, this is the study of sampling and referencing and why.
In digital environments, intertextuality becomes literal and functional. Code libraries reference other code libraries. Websites link to and embed other websites. APIs allow different platforms to communicate and share data. A single digital text might pull content from multiple sources simultaneously – a Twitter embed, a YouTube video, a Google Map – creating a networked document that exists across multiple platforms and authors. Virality builds rhetorical velocity through layers of meaning being added by individual users in real time, creating new texts and contexts through iteration and sharing.
Writing makes reality
A lot of students of this era took up knitting. It was trendy, yes, but the professors also taught knitting as an applied example of technical writing, and how writing produces a material reality.
Knitting patterns are technical writing in its purest form. A pattern is a set of instructions that must be precise, unambiguous, and reproducible, the same goals as any technical document. Pattern writers use specialized notation (K2tog, SSK, yo) that functions like code, compressing complex physical actions into standardized symbols that individuals interpret using sticks and string. The pattern must account for different skill levels, anticipate common errors, and provide enough context for the knitter to understand not just what to do, but why.
Good instructions and an able translator may result in a wearable delight: a sweater, a scarf, a cozy and colorful pair of socks. When a pattern fails the result is the same as failed technical documentation: confusion, wasted time and an unusable product. Piles of string. Dumb, useless sticks. It is an incredibly strong reminder that technical writing isn’t confined to manuals and protocols. It exists anywhere complex processes need to be communicated clearly and consistently so others can replicate results – including in your granny’s yarn basket.
So that’s how I learned to knit. Digital rhetors link physical practices to digital ones to illustrate highly conceptual ideas about writing and social networks. And one reason why digital spaces like Ravelry deserve recognition as thoughtful, functional social platforms is that this link between conceptual and material is made explicit in the digital knitting community. Designed for information sharing among a particular audience, decisions about information architecture and community management reasonably cascade from the mission, so Ravelry has remained a reasonably healthy community experience for most users despite its massive size and sprawling discussion. It remains an example of positive social dynamics online, unlike its behemoth competitors.
Always returning to Haraway
Many thinkers and texts built out this field of thought, but Cyborg Manifesto sits at the forefront for me. Writing during the Reagan era, with the populace freaking out about the rise of biotechnology and personal computing all around her, Haraway entered debates about whether women should enter male-dominated, militaristic fields like engineering and computer science, bringing an overtly feminist lens to questions of technology and power.
One major takeaway from Haraway’s work is the importance of rejecting binary thinking around technology and science. This approach aligns with other humanist and feminist perspectives that foundationally believe technology is by, about, and for the human experience, thus providing new and novel sites for political struggle. This gave people frustrated by tech a permission structure for interacting with technology rather than avoiding or abstaining from it entirely.
If these questions of knowledge and power remain central to technology, we want the people making those decisions to share our values and interests, and to be in the room when decisions are made. This argument is ripe for various challenges, which is why it was such a provocative starting point for cyberpunks and cyberfeminists alike.
Sharing is caring
This was an open source culture that meant sharing not just finished products, but the breadcrumbs and other attempts at learning along the way. It requires the safety that supports a yes/and culture, where people can collaborate with transparency, in spite of, or in consideration of, the ugly stuff and the many unknowns.
We let public and private live alongside each other without rigid boundaries about professionalism and polish. Your serious professional work could sit next to a meme, which could sit next to a picture of your cat, and none of it diminished the other.
This was an intentional acknowledgment that people are multifaceted, and that the digital spaces we inhabit should reflect that complexity. Putting the personal and the real alongside the artificiality of digital communications builds a relationship between the viewer and creator in ways that carefully curated, brand-managed presences just can’t (also: yawn).
Vulnerability, humor, expertise, horror, scholarship, and joy coexist, as in real life.
I have a confession. While experimenting with AI over the last year, I wondered what would happen if I crammed an unfinished novel draft, one I actually care about, into Claude. Claude is pitched as the LLM for writers, with Claude 3.7 Sonnet and 3 Opus widely regarded as the premier LLMs for writers, including creative writing, long-form content and human-like prose. Meanwhile, I majored in English and work in mass communications, so I’m trained to think about writing creatively, strategically and tactically. Writing and personal expression have been part of my daily life for most of my life. If this tool could in fact produce a quality story, someone like me should be able to make it happen. Instead, the experience left me confident that AI isn’t a good vehicle for creative, narrative writing.
Here’s what I found:
On the technical side, Claude struggled to maintain a narrative thread over time. The longer the chat, the more the bot drifted and eventually lost track of details and claims made about characters earlier in the plotline. It’s not a sustainable approach for narrative writers because continuity matters: outsource too much plotline to the bot and your characters lose relationship to one another.
LLMs like Claude work fine for writing support—they can function something like a synonym machine, helping writers work through technical questions of redundancy, register, length, and other semantic needs while drafting. But when you outsource world-building and meaning-making to an LLM, it becomes narratively confusing fast. Despite giving Claude extensive background on my primary characters and the world they live in, it would confidently declare that a character’s relationship to another was X, then claim the opposite on the next page. Dialogue was thin and expository. It preferred a sort of “maid and butler” style of dialogue where two characters artificially recap shared knowledge for the reader. Meanwhile Claude does not do feelings well, which is arguably the point of much narrative writing.
Ultimately my drafts were worse off than what I started with – less organized, more confusing, with so much narrative drift that almost nothing was usable, even as a first draft. A devil’s advocate might argue that my prompting wasn’t sophisticated enough to produce the results I wanted. Sure.
But then we have the second problem: Claude’s approach to storytelling isn’t narratively interesting. Fiction and narrative writers put tremendous energy into world-building and sensory experiences. The goal is to immerse the reader in a sensory experience so total that they can experience another world entirely – the original VR, if you will. A great writer even exploits your higher-level cognitive functions by reusing parts of the brain that evolved for action and perception, which is why a good story makes you think, feel, and wonder.
Claude does not feel or wonder. Claude collates.
A key part of this essay suggests that LLMs create meaning through triangulation – that by pinging other ideas and vocabulary, an LLM can get a human reader close, or close enough, to suffice in many cases of writing. In my experience, this is true enough in business writing, where tinkering with approach and register can become as important as precise verbiage.
But this misses the pleasure and the point of good storytelling, which is myriad but usually centers on the satisfaction of expanding your imagination and experience through narrative, by seeing your own messy, striving, failing, hopeful, and collective human experience reflected in another person’s expression. That kind of meaning-making doesn’t happen through triangulation. It happens through the labor of human thought, experience and skilled articulation. That’s art, babes.
This article gets into the mess of AI and creative writing, within the domain of the romance genre, which famously cranks out variations on romance themes at a rapid clip. It drills down into some of the debates about writing, authority and authorship in relationship to LLMs that are playing out across the publishing sector now. Remember: early research suggests that most writers who use LLMs as part of their workflow ultimately retain their sense of authorship in and around the tools, suggesting that even when writers adopt AI assistance, they still see themselves, not the tool, as the creative and accountable source. So based in my experience above, I suspect that if an AI approach to creative writing is successful, it’s because the author is linking her approach to emerging tech, not because the work is good, and that’s a difference worth distinction.
This PBS doc recently came to Netflix, and I tucked in expecting a nice but predictably boring documentary about one of my favorite authors. To the contrary, it really plumbs Morrison’s writing craft, not only as an author but as an editor who brought a generation of incredible thinkers into the limelight. She talks in depth about her writing process, her approach to authorship and editing, and how she kept these roles separate at the peak of her midcentury literary career in NYC’s publishing industry.
There’s something so powerful about hearing her discuss the craft, her deliberate choices, the refusal to center whiteness, and the insistence that Black readers were her intended audience. She saw her critics and wrote around their critiques with authority and confidence. This was a breath of fresh air since I’m so immersed in the AI era, which threatens to change our perceptions around value of writing, the choices and experiences behind the author, and how we consider the influence and responsibility of authorship.
There is something about the AI moment that reminds me a lot of when the internet was new. A lot of what was imagined and promised about the internet was never realized. But much was.
I’ve been reading Ellen Ullman’s memoirs - “Life in Code” and “Close to the Machine” - and her observations about proximity to technology feel relevant here. Being close to the machine means understanding its actual capabilities and limitations apart from the prevailing sales narratives. It also means a kind of loneliness, because you are working in a space that others don’t yet see clearly or fully understand.
I suspect people thinking seriously about AI right now will experience something similar: a stretch of hostility and discomfort while the rest of the world catches up and the consumer market level-sets on the promises being made. In the interim, the hype will not match the reality, and the reality will sometimes exceed the hype in ways no one predicted. And for a while, how it works and why it matters won’t be legible to everyone at once.
The move from a structuralist account in which capital is understood to structure social relations in relatively homologous ways to a view of hegemony in which power relations are subject to repetition, convergence, and rearticulation brought the question of temporality into the thinking of structure, and marked a shift from a form of Althusserian theory that takes structural totalities as theoretical objects to one in which the insights into the contingent possibility of structure inaugurate a renewed conception of hegemony as bound up with the contingent sites and strategies of the rearticulation of power.
This was my era in college, and I remember feeling challenged and overwhelmed as an undergrad trying to read theory for the first time. So today, I asked Claude.ai to put it in plain language, just to see what would happen. Here’s the after:
Scholars used to think that capitalism shaped society in consistent, predictable patterns. But newer thinking focuses on how power works—how it repeats, overlaps, and gets reshaped over time. This brought time into the picture and moved away from older theories that treated social structures as fixed. Instead, recognizing that structures can change opened up a new way of understanding power: it's tied to specific moments and strategies, and it's always being renegotiated.
French overlooks how smartphones and social media raised the stakes on debate and discussion, transforming campus discourse. Today’s students worry that one viral misstep (in countless directions) may define them forever.
Tiktok is not much better or worse than other major social platforms, I say. The primary arguments against TikTok, including data collection, algorithmic manipulation, potential foreign government access, addiction and influence on public opinion, apply with equal or greater force to American platforms. Meta has faced billions in fines for allowing privacy violations, enabled documented election interference, and its algorithms have been linked to mental health harms and the amplification of extremist content globally, including perpetuating a genocide in Myanmar. Google and other domestic platforms vacuum up vastly more user data with fewer restrictions.
The distinguishing factor isn’t the behavior but the ownership: TikTok’s parent company ByteDance is subject to Chinese law and intelligence relationships, while Meta and Google are subject to U.S. law and intelligence relationships. That’s a legitimate policy distinction, but rarely articulated honestly. Instead, the debate has been framed around purportedly unacceptable harms that American tech companies perpetrate routinely, creating a kind of security theater that lets domestic platforms escape equivalent scrutiny while positioning a foreign competitor for a forced sale or ban.
The TikTok deal means American users will see a US-only algorithm. Brands and creators will likely see smaller audiences and higher costs for domestic reach. ByteDance faces split algorithms, divided workforces and parallel governance, complicating product delivery across global markets.
“In short, decades of research have demonstrated that our political beliefs and behavior are thoroughly motivated and mediated by our social identities: i.e., the many cross-cutting social groupings we feel affinity with. And as long as we do not account for this profound and pervasive dependence, our attempts to address the epistemic failures threatening contemporary democracies will inevitably fall short. More than any particular institutional, technological, or educational reform, promoting a healthier democracy requires reshaping the social identity landscape that ultimately anchors other democratic pathologies.”
As always, this drives me back to Haraway’s cyborg, a useful metaphor for thinking about our political, environmental and social tangle and how it butts up against emerging tech and science. (In Haraway’s context, it was the rise of STEM as a driving force in academia at the dawn of the computer age.) Bagg’s argument lands in familiar territory for anyone who’s wrestled with the cyborg metaphor. Both reject the assumption that better information alone will save us from ourselves, whether from context collapse or the dualisms (binaries, heh) that structure how we think about technology, nature, humanity and politics.
Bagg arrives at something parallel from political science: We trust information that affirms the groups we belong to.
Business and marketing, for what it’s worth, tell us the same thing from a slightly different angle: you’re most likely to convert on a recommendation from a trusted friend.
The next best thing in our current digital media landscape: a trusted influencer you identify with, which is why TikTok increasingly feels like QVC.
The problem isn’t that people lack access to truth, it’s that they’ve lost affinity with the experts, institutions and collaborative practices that produce real expertise. We’re arguably in era so thick with marketing that we are locked into performing respectability and purity aesthetics as our primary approach to the world and one another, instead of prioritizing social health and good function.
Both perspectives point toward the same conclusion: you have to recognize shared affinities through the slow work of creating conditions where people want to trust each other across differences.
The CEO of Instagram says that social media platforms will be under mounting pressure to help users tell the difference between human-made and AI content, and that going forward, it will be more practical to label real content over AI.
I suspect these three trends are connected: Women reportedly use AI at significantly lower rates than men—25 percent lower on average—in part because they’re more concerned about ethics, including privacy, consent and intellectual property. At the same time, countries with more positive social media experiences tend to be more open to AI, while Americans’ distrust is shaped by years of watching tech platforms erode trust. Meanwhile, one of the largest social platforms has turned its AI chatbot into a harassment tool—generating roughly one nonconsensual sexualized deepfake image per minute, disproportionately targeting women and girls.
Winer’s original script is pretty slick, and includes a list of all my feeds with titles, URLs, and categories; click-to-expand functionality to see the 5 most recent posts from each feed; clickable post titles that open articles in new tabs; sort options (by title or by update); and automatic updates when I change my FeedLand subscriptions.
The official documentation method didn’t initially work because Hugo (the blogging software behind micro.blog) was wrapping client-side templates around the script. The toolkit requires server-side dependencies that don’t exist on static sites like micro.blog, and we hit a cascade of missing JavaScript dependencies (jsonStringify, servercall, etc.). Each fix revealed another dependency, leading to some “sunk cost” frustrations for me. I kept trying because I wanted to see if Claude could pull it together. Through trial and error, I got to a point where the OPML file was rendered correctly without server dependencies or complex external libraries.
Time invested: ~3 hours (including wrong turns)
Time it should take: 10 minutes
AI extended my code reach beyond my practical skillset by quite a lot. I now have a dynamic and dedicated place to read and share news feeds as I wish. Though even when generative AI works and works well, I have significant concerns about the intellectual property implications of AI, and this project brought those tensions into sharp focus. The AI could only help me because it was trained on documentation and intellectual work from the open source community, contributions made freely in the spirit of knowledge sharing, not to train commercial AI systems. I tapped into their expertise by paying Anthropic $15 a month. While I’m grateful for the accessibility this provides to non-developers like me, I recognize there’s an unresolved ethical question about whether this use respects the intent and labor of the original creators. The feat is incredible; the foundation it’s built on deserves careful consideration.
After the exercise was complete, I asked Claude how I could have improved my prompting to make this process easier, and in short, Claude said I could have been a web developer. But since I’m not, here’s what it recommended:
✅ When the process isn’t working, question the process mid-stream. Most people either give up or keep following bad advice deeper into rabbit holes. Stop and question the LLM’s process and ask for alternatives to force a reset.
✅ Push for usability. Keep bringing the conversation back to what you actually need the end result to do, not what’s technically impressive or “correct.” In my case, this meant repeatedly asking “can I click through to the articles?” rather than getting lost in discussions about CORS proxies or JavaScript syntax. Focus on outcomes, not implementation details.
✅ Ask for complete solutions. Instead of trying to mentally patch together incremental changes across multiple responses, ask the LLM to provide fresh, complete code each time. This prevents copy-paste errors and ensures you’re always working with a coherent, tested solution. There’s more than one way to crack an egg, but you want the whole egg regardless.
After all that, I got it to work but can’t figure out how to make it show up in my header menu, with or without Claude. TBD.
In recent history I stopped posting on most social media and moved to the fediverse. I still browse the social platforms to keep up with trends and friends, but I only post on my private IG and here.
What I share here is separate from but related to my professional life — I’m thinking out loud and making room for rough, unfinished ideas. I write mainly for myself, but if others find it useful, that’s great. The practice of reading and reflecting makes your thinking stick, and I am from a certain time and place, so this is how I approach learning and communicating about what I’m learning. It’s a habit.
While this is my preferred approach, I acknowledge that sharing unfinished ideas publicly is risky and you have to accept accountability for the messiness that comes with that. But I also know that working through your vulnerability through the act of writing lets you tap into your most creative, innovative self and test your ideas against an evolving sense of what’s good. The potential for an audience, however real or implied, keeps you more honest and less self-indulgent. Despite the trade offs, I think it’s worthwhile.
While I’m cleaning up the cruft around my social presence, I’m finding more references to the heyday of blogging that explain how people organized online (Web 1.0) before the era of platforms (Web 2.0). One is this interview with Jill Filipovic, my one-time co-blogger and comrade at Feministe, with the folks at LGM who interviewed me on the subject a few years ago. Jill and I differed (and still do) on the meat of many issues, but have a lot of our thinking in common about how to handle disagreement and advance our ideas in common (and risky!) spaces. She’s welcome in my foxhole anytime.
I appreciate this oral history project by LGM because the articulation of our processes then (before automation) provides a lot of instruction about how to organize and think about communication outside of increasingly toxic and irresponsible social platforms today. One idea that is relevant today is around pluralism. In our case, we practiced pluralism on subjects and perspectives within a defined domain, “feminism.” In practice, making it work before true automation meant we lived with a lot of decision points around moderating a raucous community. Back then, we had our CMS and set up a list of community expectations, iterating as we went, then let the community rip. Collectively, this approach created a robust and vibrant interactive community of peers and moved our interests forward as a cohort. Between her interview, where she talks through the decision points we faced, and mine, where I talk more about the tools and their limitations, there is a lot for people who want to organize people digitally while thinking outside of the algorithm.
There are certain pieces of writing I return to when thinking about our relationship with technology. Donna Haraway’s “A Cyborg Manifesto,” published in 1985, is one of them. Despite being forty years old, it continues to offer insights into how we understand technology’s role in our lives.
Haraway used the cyborg, a hybrid of machine and organism, as a metaphor for understanding identity in an increasingly technological and scientific world. Her central argument was this: because the traditional boundaries we’ve relied on are breaking down with the rise of STEM, computers and factory automation, a tenuous new order is emerging, blurring the lines between human and machine, physical and digital, natural and artificial, gender and biology, moral and immoral. The world order was undergoing a remodel, the guys were designing the game board for the next era, and she worried that refusal meant catastrophic and strategic losses to research, creating the cyborg metaphor to challenge her cohort to consider her angle. Many women in academia resisted the political push for STEM, concerned about the impact on the humanities. This was critical stuff in the mid-1980s, especially in a global (and academic) context of collective civil rights struggle across very different coalitions, amid the science and all of its implications. She wrote it as a salvo appealing to fellow academic feminists not to be so skeptical of new and emerging computer technology that they lose on emerging opportunities. To her, the implication of these new technologies meant new political landscapes and platforms for discussion and iteration.
Haraway challenged the either/or categories that dominate these debates: online versus offline, human versus machine, authentic versus artificial, even good and bad. Instead, she proposed we’re already living in a world of hybrids and overlaps and contingencies and compromises, where identity and experience are shaped by our relationships with technology and science and capitalism rather than existing separately from it. Whether you wear glasses, take daily medicine, strum a guitar, drive a car, or regularly log into a device for work or leisure, our lives are heavily augmented by layers of tech already. Your cyborg self is already here. We are already deeply technical creatures, living in concert with machines.
Haraway invites us to dabble in the Matrix, to take off your trench coat and stay awhile, to see what it feels like in the moral relativism and ambiguity. Within this web of complexity lies a lot of opportunity.
Tl;dr: the cyborg metaphor is a permission structure and a thought exercise: Instead of asking if or whether to accept tech, she asks you to consider something more pragmatic, how your cyborg self might use and shape technology to assert your particular existence, politics and interests across the network. This is an if/then that is simultaneously empowering, cynical, dystopian, ironic and futurist, but allows us to set aside some limiting binaries and narratives when thinking about the specter of new technology.
I miss Twitter because I’m full of big opinions and inappropriate one-liners. Anyway, here’s an analysis from WIRED on the implications of this month’s AWS outage.
Very much enjoying the Netflix series “Dark Winds,” based on the series of novels by Tony Hillerman. Not only does the storytelling slap, but it’s visually beautiful, with sweeping Southwest vistas and a fleet of classic vehicles that look so good in widescreen.
Purdue Exponent students distributed 3,000 copies of a special “solidarity edition” newspaper in Bloomington after IU spanked their student paper for insubordinance, ending the IDS print edition and firing their director. The media landscape in Indiana is bleak, generally, after years of disinvestment, so student reporters fill a social and political gap that the free market left behind. Given those conditions, the wider community depends on student media, much like public radio, to fill the information gaps. Also, these campuses are situated in communities where it can be very socially uncomfortable to be a squeaky wheel. So. As alum, I’m proud of the Exponent for this brave and newsworthy show of heart. 💐
Gap capitalized on the Sidney Sweeney “good genes” controversy by doing this collab with Katseye, the global pop group where each member is a different nationality. Each member of the pop group dances through in black and brown jeans.
I graduated from college right before the 2008 recession and bounced through some unpromising temp jobs until an opportunity emerged for a permanent position. Sometimes you just need to get in where you fit in, and so I did. That’s how I came to work for a regional cable company that used federal money to expand the new national broadband network, extending out to the rural communities dotting central Indiana.
It was a front row seat to the national broadband expansion efforts of the early 2000s. Our business ran right across the state, spanning the 80 or so miles from Attica to Kokomo, which included several small cities with large manufacturers, two public research universities, and several community and liberal arts colleges. The strip of broadband fiber at the core of our service followed existing highways and electrical lines that split the corn and soybean fields from town to town, feathering out to more rural areas from there.
I worked a variety of roles there that put me face-to-face with a classic technical problem: the last mile. On many occasions, someone would come in looking pensive, and explain that the fiber had been extended all the way from town to their hamlet, and yet there was no plan to connect their property to the pole. Over time the pattern was clear: while the network was expanded, the cost of running a physical line to each individual property was too expensive and specific at scale. These customers often left without a path forward despite all their efforts and ours.
The “last mile problem” refers to the logistical challenges and high costs associated with the final leg of delivering goods or services to the end customer. It’s often the most difficult and expensive part of the supply chain, despite being a relatively short distance. The pattern shows up everywhere: public transit can get commuters most of the way most of the time, but that final leg of the journey remains specific and individual and problematic. E-commerce companies promise drone delivery solutions, scooter and bike-share apps claim to solve urban mobility gaps, but these technological optimizations remain persistently stubborn at scale, running up against the messy realities of sidewalks, intersections, and actual human behavior.
Tl;dr: I’ve been turning over this suspicion that AI automation will hit a classic “last mile problem,” especially in the public sector.
AI systems, particularly LLMs, are like those systems—they work incredibly well in their intended domain, processing and manipulating information. But because they’re fundamentally an information-only approach, that creates their own last mile problem when we try to implement them in physical and context-specific environments. Public institutions are uniquely specific — they are often the originators and producers of knowledge and the keepers of original policy, tasked with making the rubber hit the road. Additionally, the need for comprehensive data protection required by public workers and institutions fundamentally hamstring potential applications.
Actually implementing recommendations is where you hit the last mile. This is the work of public administration.
Imagine an AI application trained on every facilities management manual ever written and tuned to synthesize best practices for HVAC optimization. It can analyze years of energy usage data and recommend precise temperature adjustments for different zones of a building, but can’t feel that the third floor is always stuffy, or know that the facilities manager retired last year and took decades of institutional knowledge with him. Building A’s HVAC system was installed in 1987 and has a manual keypad. Building B’s system interfaces with the campus-wide monitoring system, but unreliably, and investigation is slated for later, someday, when resources allow. Professor Smith has taught in Room 204 for 25 years and will blow up your spot before moving to a different classroom for maintenance. Your engineers who manage these spaces are balancing human teams, who have time off and training and other priorities they manage in life. So, you need staff who understand the quirks of each area, the history of each system, the politics of which departments will accept changes and which will flood your inbox with complaints. You need someone who knows that the third floor always runs hot because of a design flaw from 1974, and that the solution isn’t more precise control but a $50,000 renovation that’s been deferred for a decade because a glittering new project across campus takes priority.
Imagine this tangle of questions and contingencies times infinity on every university campus in existence. Universities are like cities—they’ve been built and rebuilt over decades or centuries, with layers of systems and fiefdoms that weren’t designed to work together. AI recommendations assume a level of standardization that simply doesn’t exist. Every AI implementation in higher ed requires navigating multiple constituencies with different priorities and power structures. It’s like trying to redesign traffic patterns in a neighborhood where the residents, business owners, commuters, and city planners all have veto power and conflicting interests.
So. When looking at efficiency efforts spinning up across the education sector, I’m feeling pensive, trying to understand how exactly the house gets connected to the pole.
The promise of new tech in higher ed needs to more deeply consider the translation costs: the human labor, institutional knowledge, knowledge documentation and local adaptation required to bridge between the usefulness of tech and specific realities of public university work. Public employees want modernization and don’t want to fall behind. We want systems that work. We are also balancing a great deal of change and pressure as a sector, with fewer material resources than ever. We need less marketing and more right-sizing in the claims around AI against the political and tech realities of public administration.
This disconnect between technological promise and implementation reality becomes even more critical as higher education faces increased political scrutiny. When tech vendors promise that AI will solve efficiency problems or reduce administrative costs, institutions are under immense pressure to deliver measurable results quickly. But the translation costs we experience don’t disappear just because the political pressure to modernize increases.
The institutions that thread this needle will be the ones that accurately assess these translation costs upfront and set expectations accordingly—not the ones that assume the technology will magically bridge the gap between digital and physical, abstract and specific.
A quick read by a singular voice, heavy on descriptions of the New York art and music scene of the 90s. Like many punk memoirs, it’s a tribute to the many names that made the movement and a memorial for a city that no longer exists. Gordon’s voice is kind and bold, curious and smart. Her descriptions of growing up in LA and coming of age in New York are painterly and poetic. Her takes are generous for all but one person: her ex-husband.
All my respect to her for opening and closing the books with her raw reflections on Thurston’s mundane and deeply uncool betrayal.
